Tracking Antibodies

An ongoing experiment to track COVID-19 antibodies over time

Introduction

Objective: To Monitor changes in 1) neutralising antibody levels 2) rate of waning 3) days to seronegative status.

How do Antibodies Work?

Code

vembedr::embed_youtube(id = "qCRwuxDpthY",
                       allowfullscreen = T) %>%
  use_align("center")

Neutralizing Antibodies Against Coronaviruses

Code

knitr::include_graphics("img/PBjorkman-CBarnes.jpg")

Credit - C. Barnes / Björkman laboratory

Dr. Pamela Bjorkman

Code

vembedr::embed_youtube(id = "Sp1aVTj7IvI",
                       allowfullscreen = T) %>%
  use_align("center")

Results

Code

sero_raw <- readxl::read_excel("~/Downloads/Santanu_COVID_events_research.xlsx", 
                               sheet = "long") %>% 
  clean_names() %>% 
  mutate(date = ymd(date))

sero <- sero_raw %>% 
  rename(start = date,
         content = event) %>% 
  mutate(sero = parse_number(outcome)) %>% 
  mutate(content = str_remove(content, "SARS-CoV-2 ANTI-SPIKE IgG ANTIBODIES")) %>% 
  mutate(content = str_remove(content, "\\:")) %>% 
  mutate(content = str_remove(content, "\\*"))

Timeline

Code

sero %>% 
  timevis()

IgG Antibody levels over time

Code

fig_igg <- sero %>% 
  filter(!is.na(sero)) %>% 
  ggplot(aes(start, sero)) +
  geom_line()  +
  geom_point(aes(size = sero)) +
  scale_x_date(labels = scales::date_format("%Y %b")) +
  labs(title = "Trend in IgG antibody levels",
       x = "",
       y = "IgG Antibodies (in AU/ml)",
       size = "") +
  theme(legend.position = "top")

ggplotly(fig_igg)